Abstract:
An electrode active material including an ordered mesoporous metal oxide; and at least one conductive carbon material disposed in a pore of the ordered mesoporous metal oxide. Also, an electrode including the electrode active material, and a lithium battery including the electrode.
Abstract:
An electronic apparatus is disclosed. The electronic apparatus includes: an image projector, a memory storing one or more instructions, and at least one processor comprising processing circuitry operatively connected with the image projector and the memory. At least one processor, individually and/or collectively, is configured to execute the one or more instructions, and is configured to: perform a keystone correction by projecting a test image to a screen, identify screen-based coordinates corresponding to each of a plurality of pixels included in the test image based on the keystone correction, identify a screen-based area corresponding to each of the plurality of pixels based on the identified screen-based coordinates, identify a reference pixel from among the plurality of pixels based on the screen-based area corresponding to each of the plurality of pixels, and identify brightness correcting information corresponding to each of the plurality of pixels based on a screen-based area of the identified reference pixel and a screen-based area of each of the plurality of pixels.
Abstract:
An electronic device includes: one or more processors; and memory, storing: a first training data set including pieces of 2D pose data and pieces of 3D pose data; and instructions that, when executed by the one or more processors, cause the electronic device to: train a neural network model to estimate 3D poses based on the first training data set; obtain an augmented data set by augmenting the first training data set; based on at least one of similarity or reliability of 3D pose augmented data in the augmented data set; select at least one piece of 3D pose augmented data among pieces of 3D pose augmented data in the augmented data set; obtain a second training data set including the 3D pose augmented data and 2D pose augmented data corresponding to the 3D pose augmented data; and retrain the neural network model based on the second training data set.
Abstract:
Disclosed is an electronic apparatus. The electronic apparatus comprises: an image projection unit; and a processor which controls the image projection unit to project a test image including a plurality of markers onto a projection surface, identifies first information indicating the position of each of the plurality of markers in the test image, second information based on a captured image captured of the projection surface from an external device, and information about a guide area, and performs keystone correction so that an image corresponding to the guide area is projected based on the first information, the second information, and the information about the guide area.
Abstract:
Disclosed are an electronic device and a method for controlling an electronic device. Specifically, the present disclosure relates to: an electronic device configured to input an acquired image into a trained artificial intelligence model, acquire information about the image from a plurality of classifiers which are included in the artificial intelligence model and correspond to a plurality of layers classified according to higher and lower concepts of an object included in the image, train the artificial intelligence model on the basis of the information about the acquired image, and perform image recognition by using the trained artificial intelligence model; and a method for controlling an electronic device.